MOTIVATION: Due to their high genomic variability, RNA viruses and retroviruses present a unique opportunity for detailed study of molecular evolution. Lentiviruses, with HIV being a notable example, are one of the best studied viral groups: hundreds of thousands of sequences are available together with experimentally resolved three-dimensional structures for most viral proteins. In this work, we use these data to study specific patterns of evolution of the viral proteins, and their relationship to protein interactions and immunogenicity. RESULTS: We propose a method for identification of two types of surface residues clusters with abnormal conservation: extremely conserved and extremely variable clusters. We identify them on the surface of proteins from HIV and other animal immunodeficiency viruses. Both types of clusters are overrepresented on the interaction interfaces of viral proteins with other proteins, nucleic acids or low molecular-weight ligands, both in the viral particle and between the virus and its host. In the immunodeficiency viruses, the interaction interfaces are not more conserved than the corresponding proteins on an average, and we show that extremely conserved clusters coincide with protein-protein interaction hotspots, predicted as the residues with the largest energetic contribution to the interaction. Extremely variable clusters have been identified here for the first time. In the HIV-1 envelope protein gp120, they overlap with known antigenic sites. These antigenic sites also contain many residues from extremely conserved clusters, hence representing a unique interacting interface enriched both in extremely conserved and in extremely variable clusters of residues. This observation may have important implication for antiretroviral vaccine development. AVAILABILITY AND IMPLEMENTATION: A Python package is available at https://bioinf.mpi-inf.mpg.de/publications/viral-ppi-pred/ CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying repetitive gene sequences occurring within DNA sequences that span a collection of species is a challenge that is conceptually simple yet computationally challenging. Biological research suggests that certain regions within genomic sequences may be unchanged for hundreds of millions of years; understanding and identifying these highly preserved regions is a major challenge faced by bioinformaticians. Taking an evolutionary perspective on DNA, pinpointing these repetitive sequences is the first step to understanding functional similarities and diversities. The difficulty of this problem arises from the volume of the data required for analysis; it grows with every genome that is sequenced. Traditional approaches used to identify repetitive sequences often require the pair-wise comparison of chromosomes, which takes a significant amount of time to gather results. When comparing n chromosomes, n(n-l) individual comparisons must be made. To avoid exhaustive pair-wise comparisons, we designed an algorithm that partitions genomic sequences into search key values representing potential repetitive sequences, which are hashed into bins. With the introduction of new genomes, we only process the new sequences and aggregate new results with those that were previously processed.